Computer Science > Robotics

Title:Expanding Motor Skills through Relay Neural Networks

Abstract: While the recent advances in deep reinforcement learning have achieved
impressive results in learning motor skills, many of the trained policies are
only capable within a limited set of initial states. We propose a technique to
break down a complex robotic task to simpler subtasks and train them
sequentially such that the robot can expand its existing skill set gradually.
Our key idea is to build a tree of local control policies represented by neural
networks, which we refer as Relay Neural Networks. Starting from the root
policy that attempts to achieve the task from a small set of initial states,
each subsequent policy expands the set of successful initial states by driving
the new states to existing "good" states. Our algorithm utilizes the value
function of the policy to determine whether a state is "good" under each
policy. We take advantage of many existing policy search algorithms that learn
the value function simultaneously with the policy, such as those that use
actor-critic representations or those that use the advantage function to reduce
variance. We demonstrate that the relay networks can solve complex continuous
control problems for underactuated dynamic systems.